Aim: The objective of this research paper is to detect the spam comments on YouTube videos with an enhanced accuracy rate by using Novel Decision Tree (D-Tree) in comparison with K-Nearest Neighbor (KNN) Classifier. Materials & Methods: The data set in this paper utilizes UCI machine learning repositories. The sample size of predicting the spam comments on YouTube videos with enhanced accuracy rate was sample 80 (Group 1=40 and Group 2 =40) and calculation is performed utilizing G-power 0.8 with alpha and beta qualities are 0.05, 0.2 with a confidence interval at 95%. Predicting the spam comments on YouTube videos with enhanced accuracy rate is performed by Novel Decision Tree (D-Tree) whereas the number of samples (N=10) and K-Nearest Neighbor (KNN) were the number of samples (N=10). Results: The Novel Decision Tree (D-Tree) classifier has 94.47% higher accuracy rates when compared to the accuracy rate of K-Nearest Neighbor (KNN) is 86.91%. The study has a significance value of p<0.05 i.e. p=0.0291. Conclusion: The Novel Decision Tree (D-Tree) provides the better outcomes in accuracy rate when compared to K-Nearest Neighbor (KNN) for detecting the spam comments on YouTube videos with enhanced accuracy rate.